I’ve been trying to build a business case for moving our processes to an open-source BPM, and honestly the finance conversation keeps stalling because we can’t pin down what the real ROI looks like.
The challenge is that most of our workflows are still locked in people’s heads or scattered across spreadsheets. We’d need weeks of analysis just to map them out properly. But I ran across something interesting—using an AI copilot to describe a workflow in plain language and have it generate something we can actually model.
The thing is, if we can go from “describe the process” to “here’s a runnable workflow” in hours instead of weeks, that’s a huge cost save right there. But I’m struggling to figure out how to quantify that in a way that doesn’t sound like I’m making up numbers.
Have any of you tried using workflow generation to actually model what a migration would cost? How did you present those numbers to finance without them asking a million follow-up questions about assumptions?
I dealt with this exact problem about a year ago. The thing that helped was stopping trying to predict the whole migration upfront. Instead, we picked three critical processes, generated workflows for each one in a workshop setting, and then timed how long it took versus our old estimation process.
Turned out the actual time difference was about 70% faster than manual mapping. Then we extrapolated that to our full process portfolio and came up with a realistic savings number. Finance ate that up because it was based on observable data, not theory.
The key was showing them a before-and-after side by side. Not just saying “AI makes it faster,” but actually running the same workflow both ways and showing the time delta. That’s what made it defensible.
One thing nobody told us upfront: the labor cost savings are real, but they’re not where most of the ROI actually comes from. What hit harder was the operational benefit. Workflows that used to take three rounds of refinement got locked in one pass. That meant faster time-to-deploy for new processes.
When you model end-to-end, you also catch inefficiencies you didn’t know existed. We found three processes that could be combined into one. That’s the stuff that makes finance say yes, not just the raw hours saved in development.
The mistake we made initially was trying to include everything in the ROI calculation. Focus on what changes materially. For us, that was developer time freed up to do higher-value work, plus the speed gain in deploying new automations. We built a simple model: current process map → estimated dev hours → cost per hour → compare that to the new approach using AI copilot. Then stress-tested it by running three actual workflows through both methods. Finance wants observable stuff, not projections. Give them that first before you talk about anything else.
Calculate the cost of your current workflow discovery process. If you’re spending 40 hours mapping a process manually, and AI copilot reduces that to 4 hours of refinement, that’s 36 hours recovered. Multiply by your loaded cost per hour and there’s your justification. Stack that across your top 20 processes and finance has a number they can work with. The secondary benefits—faster deployment, fewer rework cycles—are bonuses for your business case.
measure current mapping time vs ai-generated time for same workflows. calculate labor cost difference. that’s your number. finance trusts observed data over projections. test on 3 real processes first.
Benchmark your current workflow creation speed against AI-generated ones. Include dev time, QA time, and deployment time in both scenarios. That gap is your ROI foundation.
This is exactly what Latenode’s AI copilot workflow generation was built for. You describe your process in plain language, and it generates a runnable workflow instantly. I’ve seen teams cut their process mapping time from weeks to days.
Here’s what makes it work for the business case: you can actually run side-by-side scenarios. Model the old way versus the new way using real data. Then show Finance actual execution times and labor hours saved. No guessing.
Plus, with access to 400+ AI models in a single subscription, you’re not juggling separate API costs and licensing for each tool. That simplifies your cost structure too.
Start a proof of concept with three workflows. Run them both ways. That data becomes unshakeable in your business case.